In-class Exercise 5

A short description of the post.

To show your figure images in a higher resolution, fix the retina in the above code chunk above.

1. Install and load R packages

packages = c('maptools', 'sf', 'raster', 'spatstat', 'tmap', 'tidyverse')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
    }
  library(p,character.only = T)
}

More on the packages used:

• Maptools is for creating spatial* objects
• sf is for importing geospatial data
• raster is for creating a raster object
• spatstat is for performing SPPA
• tmap is for plotting thematic maps
• tidyverse is for data handling

2. Data used:

• MP14_SUBZONE_WEB_PL: a polygon feature data in ESRI shapefile format from data.gov.sg, providing information of URA 2014 Master Plan Planning Subzone boundary data
• CHILDCARE: a point feature data in rds format that provides both location and attribute information of childcare centres from data.gov.sg
• CHAS: a point feature data in rds format that provides location and attribute information of CHAS clinics

3. Import the Geospatial data

• Importing shapefile using st_read() of sf package. The output object is in tibble sf object class.

mpsz_sf <- st_read(dsn = "data/shapefile", layer = "MP14_SUBZONE_WEB_PL")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\aisyahajit2018\IS415\IS415_blog\_posts\2021-09-13-in-class-exercise-5\data\shapefile' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

We can see from the results above that mpsz_sf projection is in SVY21.

4. Import aspatial data from rds folder

• read_rds() of readr package is used instead of readRDS() of base R is used. This is because the output of read_rds() is in tibble object.

CHAS <- read_rds("data/rds/CHAS.rds")
childcare <- read_rds("data/rds/childcare.rds")

Note: There are some data issue in the childcare data frame because Lat and Lng should be in numeric data type. The coordinate fields are in decimal degrees. Hence, wgs referencing system is assumed.

5. Convert aspatial data frame into sf objects

5.1 CHAS conversion to sf object

CHAS_sf <- st_as_sf(CHAS,
                    coords = c("X_COORDINATE",
                               "Y_COORDINATE"),
                    crs=3414)

Note: st_as_sf accepts the coordinates in character data type.

5.2 Childcare conversion to sf object

childcare_sf <- st_as_sf(CHAS,
                    coords = c("Lng",
                               "Lat"),
                    crs=4326) %>%
  st_transform(crs=3414)

Note: In childcare we have Lng and Lat as the coordinates where they are both in demical degrees. The coordinate system for that is WGS84, crs code 4326. We then use st_transform to change the coordinates of a geometry from one spatial reference system to another.

6. Plot to review

Notes for arguments: - alpha is to set transparency - col is to set the colour of the dots - size is to set the size of the dots

tmap_mode('view')
tm_shape(childcare_sf) +
  tm_dots(alpha=0.4,
          col="blue",
          size=0.05) +
tm_shape(CHAS_sf) +
  tm_dots(alpha=0.4,
          col="red",
          size=0.05)

tmap_mode('plot')

Note:

• Darker blue points show that you have more than 1 childcare centers
• With the interactive mode, you can:
  • Zoom in and out to examine the data
  • Switch views to see the context like which neighbourhood they are in etc.
    • GrayCanvas to see the distribution
    • OpenStreetMap to see the local context

7. Geospatial Data Wrangling

7.1 Converting from sf to Spatial* classes

The asterisk in Spatial* is to say that we have more than 1 Spatial objects

7.2 Converting from Spatial* classes to sp format

7.3 Converting from sp format to spatstat ppp format